Image Compression via Semantic Feature Extraction and Historical Frame Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image compression technologies fail to achieve a high compression ratio while maintaining image quality, leading to poor quality of compressed images and adverse user experiences due to large image data sizes and limited bandwidths.
Innovation Solution
An image processing method that involves semantic feature extraction, matching with historical frame images, and generating compressed information packets, allowing for increased compression ratios while ensuring image quality through reconstruction processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional image compression technology is used, then image data size is reduced, but image quality deteriorates
Solution Approach 1:
The image is divided into multiple semantic regions based on semantic segmentation, and each region is processed independently with different compression strategies. Important regions (e.g., containing people or objects) are preserved with higher quality while less important regions are compressed more aggressively, resolving the contradiction between overall data reduction and localized quality preservation.
Solution Approach 2:
Different compression quality levels are applied to different spatial regions of the image based on their semantic importance. Regions containing semantically significant content (people, objects of interest) maintain higher quality while background regions use lower quality, allowing high compression ratios overall while preserving critical image quality where needed.
2Quantity of substance
If high compression ratio is achieved, then storage space and bandwidth requirements are reduced, but image quality and user experience deteriorate
Solution Approach 1:
The image processing system segments the image into semantic regions and applies differentiated compression, allowing high compression ratios for non-critical regions while maintaining quality for user-important regions, thus achieving both storage efficiency and user experience preservation.
Solution Approach 2:
The system dynamically adjusts compression parameters based on semantic content analysis. By changing compression strength, quality levels, and processing methods according to the semantic importance of different image regions, the system achieves high overall compression while preserving user experience in critical areas.
3Manufacturing precision
If detailed image information is preserved, then image quality is maintained, but data transmission and storage efficiency decreases
Solution Approach 1:
The system extracts only the essential semantic features and key visual information from each image, discarding redundant data. By taking out and preserving only the most important image characteristics needed for quality reconstruction, the system maintains image detail quality while significantly reducing data volume for efficient transmission and storage.
Solution Approach 2:
Instead of uniformly preserving all image details, the system applies high-quality preservation only to semantically important local regions while using aggressive compression for less important areas, achieving efficient transmission with maintained quality where it matters most.
Data Source
AI summary
The present disclosure relates to an image processing method and device, a storage medium and an electronic device. The image processing method includes: acquiring a current frame image, and performing semantic feature extraction processing on the current frame image to obtain a semantic feature set of the current frame image; determining a historical frame image matched with the current frame image, and acquiring frame number information of the historical frame image; and generating a compressed information packet according to the semantic feature set of the current frame image and the frame number information of the historical frame image, and storing and/or transmitting the compressed information packet. Thus, the image processing method can increase an image compression ratio while ensuring image quality, thereby allowing image information to be transmitted and stored conveniently.